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|Title:||Intelligent Technique for Grading Tropical Fruit using Magnetic Resonance Imaging|
|Authors:||Balogun, Wasiu A.|
Salami, Momoh-Jimoh E.
McCarthy, Michael J.
Mustafah, Yasir M.
Aibinu, Abiodun M.
|Keywords:||Magnetic Resonance Imaging|
Artificial Neural Network
|Publisher:||International Journal of Scientific & Engineering Research|
|Citation:||Balogun, W. A., Salami, M. J. E., McCarthy, M. J., Mustafah, Y. M., & Aibinu, A. M. (2013). Intelligent Technique for Grading Tropical Fruit using Magnetic Resonance Imaging. International Journal of Scientific & Engineering Research, 4(7), 216-225.|
|Abstract:||Recent application of modern marketing techniques coupled with intelligent agricultural systems of production has transformed small scale farming into large scale, in most part of the world. Characteristically, most of the tropical fruits, such as orange, appeared edible physically but internally such fruits might be defective based on their tissue and juice. Eventually, these fruits, via the market and undetected, usually get to the consumers who encounter the unfavourable status of the fruits. Our purpose, in this study, is to develop a non-destructive method to predict the status of orange fruits, based on internal quality. Graph of histogram showing the levels of different four colour intensities were acquired and analysed. The features extracted from Magnetic Resonance Imaging (MRI), using any of the two proposed methods, were applied as an input to train artificial neural network (ANN) in order to predict the orange fruit status. Different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were developed using|
|Appears in Collections:||Research Articles|
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